Tips for a Predictive Project

Written by Divya Krishnan, May 2018

Completing a project is always a daunting task, especially when it is predictive in nature. Sometimes even starting a predictive project can be intimidating due to the lack of experience or established standards in such projects (watch our short video on Tips for a Predictive Project here). Bsquare has years’ worth of experience in completing multiple predictive projects with their customers. Here are some tips that are taken out of Bsquare’s experience of successfully completing predictive projects.

Starting Point

The first task for a predictive project is to define the kind of predictive insight that one wants at the end of the predictive project. There are countless predictive insights that can be gathered given the right set of data but being specific in your analytic goal is of vital importance to derive maximum value from a predictive project. For defining a specific goal, the first step is to define the goal of one’s business. Some questions to ponder over are –

-Where is your business headed in the next 10 years?

-What is the business trying to do – Improve customer experience, reduce costs or improve efficiency?

-Which area does the business want to focus on first? Does the chosen area have enough data collected for analysis? How easy or difficult it is to obtain new data for analysis?

Consider an example for arriving at a specific goal for a fleet company, ABC. Assume ABC company wants to reduce cost of operation. One way to reduce costs for a fleet company is to reduce vehicle downtime. One way to reduce vehicle downtime is to reduce the time spent at the repair shop by getting smarter at diagnosing vehicles. The time spent at the repair shop can be reduced by using data to predict the right repair for the vehicle thereby short-circuiting the manual set of steps with the exact steps required to fix the vehicle in the shortest amount of time. Hence the goal of the predictive project should be to predict the repair given the vehicle operating characteristics and past repair history. This shows how one can start from a broad business use case and get to a specific goal for a predictive project.

The Three Pillars

There are three pillars for a predictive project – Data, Information and People. Data represents all data potentially relevant to the predictive project. Data can come from multiple sources – sensors, enterprise systems and product/design specifications. Exploring all possible data sources and a good discussion among data scientists and domain experts is crucial to uncovering the relevant data for the predictive project. Information represents data about data. Any piece of information that can help understand the relevant data such as data dictionaries, question-answer sessions with experts and domain discussions. It may take a few iterations to get the right data and information for the predictive project but understanding what is the relevant data and information is key to the success of the project. Last but not the least, People is the key to getting the right data and information on time. It is recommended to have a domain and a data expert on the project dedicated to unblocking potential roadblocks and assisting in the collection and distribution of data and information.

Measuring Success

Defining the right quantitative metrics for a predictive project is crucial to measuring its success. Some examples of metrics include measures of model performance such as accuracy, precision, recall, misclassification rate etc. One can also define new metrics specific to their business or domain by making changes to the calculations of the existing metrics or defining entirely new metrics from scratch.

To read the first installment in this Data Science series or watch the video, please click here.